Bad Statistics I – the phantom line

I came across this chart on the web recently.

BadScatter01

This really is one of my pet hates: a perfectly informative scatter chart with a meaningless straight line drawn on it.

The scatter chart is interesting. Each individual blot represents a nation state. Its vertical position represents national average life expectancy. I take that to be mean life expectancy at birth, though it is not explained in terms. The horizontal axis represents annual per capita health spending, though there is no indication as to whether that is adjusted for purchasing power. The whole thing is a snapshot from 2011. The message I take from the chart is that Hungary and Mexico, and I think two smaller blots, represent special causes, they are outside the experience base represented by the balance of the nations. As to the other nations the chart suggests that average life expectancy doesn’t depend very strongly on health spending.

Of course, there is much more to a thorough investigation of the impact of health spending on outcomes. The chart doesn’t reveal differential performance as to morbidity, or lost hours, or a host of important economic indicators. But it does put forward that one, slightly surprising, message that longevity is not enhanced by health spending. Or at least it wasn’t in 2011 and there is no explanation as to why that year was isolated.

The question is then as to why the author decided to put the straight line through it. As the chart “helpfully” tells me it is a “Linear Trend line”. I guess (sic) that this is a linear regression through the blots, possibly with some weighting as to national population. I originally thought that the size of the blot was related to population but there doesn’t seem to be enough variation in the blot sizes. It looks like there are only two sizes of blot and the USA (population 318.5 million) is the same size as Norway (5.1 million).

The difficulty here is that I can see that the two special cause nations, Hungary and Mexico, have very high leverage. That means that they have a large impact on where the straight lines goes, because they are so unusual as observations. The impact of those two atypical countries drags the straight line down to the left and exaggerates the impact that spending appears to have on longevity. It really is an unhelpful straight line.

These lines seem to appear a lot. I think that is because of the ease with which they can be generated in Excel. They are an example of what statistician Edward Tufte called chartjunk. They simply clutter the message of the data.

Of course, the chart here is a snapshot, not a video. If you do want to know how to use scatter charts to explain life expectancy then you need to learn here from the master, Hans Rosling.

There are no lines in nature, only areas of colour, one against another.

Edouard Manet

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The graph of doom – one year on

I recently came across the chart (sic) below on this web site.

GraphofDoom

It’s apparently called the “graph of doom”. It first came to public attention in May 2012 in the UK newspaper The Guardian. It purports to show how the London Borough of Barnet’s spending on social services will overtake the Borough’s total budget some time around 2022.

At first sight the chart doesn’t offend too much against the principles of graphical excellence as set down by Edward Tufte in his book The Visual Display of Quantitative Information. The bars could probably have been better replaced by lines and that would have saved some expensive, coloured non-data ink. That is a small quibble.

The most puzzling thing about the chart is that it shows very little data. I presume that the figures for 2010/11 are actuals. The 2011/12 may be provisional. But the rest of the area of the chart shows predictions. There is a lot of ink on this chart showing predictions and very little showing actual data. Further, the chart does not distinguish, graphically, between actual data and predictions. I worry that that might lend the dramatic picture more authority that it is really entitled to. The visible trend lies wholly in the predictions.

Some past history would have exposed variation in both funding and spending and enabled the viewer to set the predictions in that historical context. A chart showing a converging trend of historical data projected into the future is more impressive than a chart showing historical stability with all the convergence found in the future prediction. This chart does not tell us which is the actual picture.

Further, I suspect that this is not the first time the author had made a prediction of future funds or demand. What would interest me, were I in the position of decision maker, is some history of how those predictions have performed in the past.

We are now more than one year on from the original chart and I trust that the 2012/13 data is now available. Perhaps the authors have produced an updated chart but it has not made its way onto the internet.

The chart shows hardly any historical data. Such data would have been useful to a decision maker. The ink devoted to predictions could have been saved. All that was really needed was to say that spending was projected to exceed total income around 2022. Some attempt at quantifying the uncertainty in that prediction would also have been useful.

Graphical representations of data carry a potent authority. Unfortunately, when on the receiving end of most Powerpoint presentations we don’t have long to deconstruct them. We invest a lot of trust in the author of a chart that it can be taken at face value. That ought to be the chart’s function, to communicate the information in the data efficiently and as dramatically as the data and its context justifies.

I think that the following principles can usefully apply to the charting of predictions and forecasts.

  • Use ink on data rather than speculation.
  • Ditto for chart space.
  • Chart predictions using a distinctive colour or symbol so as to be less prominent than measured data.
  • Use historical data to set predictions in context.
  • Update chart as soon as predictions become data.
  • Ensure everybody who got the original chart gets the updated chart.
  • Leave the prediction on the updated chart.

The last point is what really sets predictions in context.

Note: I have tagged this post “Data visualization”, adopting the US spelling which I feel has become standard English.